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A prediction model for 30-day mortality of sepsis patients based on intravenous fluids and electrolytes

To establish a prediction model for the 30-day mortality in sepsis patients. The data of 1185 sepsis patients were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) and all participants were randomly divided into the training set (n = 829) and the testing set (n = 356)....

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Autores principales: Wang, Yan, Feng, Songqiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9524964/
https://www.ncbi.nlm.nih.gov/pubmed/36181047
http://dx.doi.org/10.1097/MD.0000000000030578
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author Wang, Yan
Feng, Songqiao
author_facet Wang, Yan
Feng, Songqiao
author_sort Wang, Yan
collection PubMed
description To establish a prediction model for the 30-day mortality in sepsis patients. The data of 1185 sepsis patients were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) and all participants were randomly divided into the training set (n = 829) and the testing set (n = 356). The model was established in the training set and verified in the testing set. After standardization of the data, age, gender, input, output, and variables with statistical difference between the survival group and the death group in the training set were involved in the extreme gradient boosting (XGBoost) model. Subgroup analysis was performed concerning age and gender in the testing set. In the XGBoost model with variables related to intravenous (IV) fluid management and electrolytes for the 30-day mortality of sepsis patients, the area under the curve (AUC) was 0.868 (95% confidence interval [CI]: 0.867–0.869) in the training set and 0.781 (95% CI: 0.779–0.782) in the testing set. The sensitivity was 0.815 (95% CI: 0.774–0.857) in the training set and 0.755 (95% CI: 0.686–0.825) in the testing set. The specificity was 0.761 (95% CI: 0.723–0.798) in the training set, and 0.737 (95% CI: 0.677–0.797) in the testing set. In the XGBoost forest model without variables related to IV fluid management and electrolytes for the 30-day mortality of sepsis patients, in the training set, the AUC was 0.830 (95% CI: 0.829–0.831), the sensitivity was 0.717 (95% CI: 0.669–0.765), the specificity was 0.797 (95% CI: 0.762–0.833), and the accuracy was 0.765 (95% CI: 0.736–0.794). In the testing set, the AUC was 0.751 (95% CI: 0.750–0.753), the sensitivity was 0.612 (95% CI: 0.533–0.691), the specificity was 0.756 (95% CI: 0.698–0.814), and the accuracy was 0.697(95% CI: 0.649–0.744). The prediction model including variables associated with IV fluids and electrolytes had good predictive value for the 30-day mortality of sepsis patients.
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spelling pubmed-95249642022-10-03 A prediction model for 30-day mortality of sepsis patients based on intravenous fluids and electrolytes Wang, Yan Feng, Songqiao Medicine (Baltimore) Research Article To establish a prediction model for the 30-day mortality in sepsis patients. The data of 1185 sepsis patients were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III) and all participants were randomly divided into the training set (n = 829) and the testing set (n = 356). The model was established in the training set and verified in the testing set. After standardization of the data, age, gender, input, output, and variables with statistical difference between the survival group and the death group in the training set were involved in the extreme gradient boosting (XGBoost) model. Subgroup analysis was performed concerning age and gender in the testing set. In the XGBoost model with variables related to intravenous (IV) fluid management and electrolytes for the 30-day mortality of sepsis patients, the area under the curve (AUC) was 0.868 (95% confidence interval [CI]: 0.867–0.869) in the training set and 0.781 (95% CI: 0.779–0.782) in the testing set. The sensitivity was 0.815 (95% CI: 0.774–0.857) in the training set and 0.755 (95% CI: 0.686–0.825) in the testing set. The specificity was 0.761 (95% CI: 0.723–0.798) in the training set, and 0.737 (95% CI: 0.677–0.797) in the testing set. In the XGBoost forest model without variables related to IV fluid management and electrolytes for the 30-day mortality of sepsis patients, in the training set, the AUC was 0.830 (95% CI: 0.829–0.831), the sensitivity was 0.717 (95% CI: 0.669–0.765), the specificity was 0.797 (95% CI: 0.762–0.833), and the accuracy was 0.765 (95% CI: 0.736–0.794). In the testing set, the AUC was 0.751 (95% CI: 0.750–0.753), the sensitivity was 0.612 (95% CI: 0.533–0.691), the specificity was 0.756 (95% CI: 0.698–0.814), and the accuracy was 0.697(95% CI: 0.649–0.744). The prediction model including variables associated with IV fluids and electrolytes had good predictive value for the 30-day mortality of sepsis patients. Lippincott Williams & Wilkins 2022-09-30 /pmc/articles/PMC9524964/ /pubmed/36181047 http://dx.doi.org/10.1097/MD.0000000000030578 Text en Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC) (https://creativecommons.org/licenses/by-nc/4.0/) , where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.
spellingShingle Research Article
Wang, Yan
Feng, Songqiao
A prediction model for 30-day mortality of sepsis patients based on intravenous fluids and electrolytes
title A prediction model for 30-day mortality of sepsis patients based on intravenous fluids and electrolytes
title_full A prediction model for 30-day mortality of sepsis patients based on intravenous fluids and electrolytes
title_fullStr A prediction model for 30-day mortality of sepsis patients based on intravenous fluids and electrolytes
title_full_unstemmed A prediction model for 30-day mortality of sepsis patients based on intravenous fluids and electrolytes
title_short A prediction model for 30-day mortality of sepsis patients based on intravenous fluids and electrolytes
title_sort prediction model for 30-day mortality of sepsis patients based on intravenous fluids and electrolytes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9524964/
https://www.ncbi.nlm.nih.gov/pubmed/36181047
http://dx.doi.org/10.1097/MD.0000000000030578
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